Process

This book presents a comprehensive coverage of remote sensing technology used to gather information on 12 types of natural hazards in the terrestrial sphere, biosphere, hydrosphere, and atmosphere. It clarifies in detail how to yield spatial and quantitative data on a natural hazard, including its spatial distribution, severity, causes, and the likelihood of occurrence. The ...

In Spatial Analysis, two distinguished authors deliver a practical and insightful exploration of the statistical investigation of the interdependence of random variables as a function of their spatial proximity. The book expertly blends theory and application, offering numerous worked examples and exercises at the end of each chapter. Increasingly relevant to fields as diverse as ...

Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique ...

The burgeoning field of data science has provided a wealth of techniques for analyzing large and complex datasets, including methods for descriptive, explanatory, and predictive analytics. However, actually applying these methods is typically a small part of the overall data science workflow. Other critical tasks include screening for suspect data, handling missing values, harmonizing data ...

The materials included in this book have been designed for a module focusing on the programming language R as an effective tool for data science for geographers. R is one of the most widely used programming languages. It provides access to a vast repository of programming libraries, covering all aspects of data science, from data ...

Testing the potential of deep learning presents unique challenges because any single application brings together various disciplines. Applying deep learning requires simultaneously understanding (i) the motivations for casting a problem in a particular way; (ii) the mathematical form of a given model; (iii) the optimization algorithms for fitting the models to data; (iv) the statistical ...

The Urban Heat Island (UHI) is an area of growing interest for many people studying the urban environment and local/global climate change. The UHI has been scientifically studied for 200 years and, although it is an apparently simple phenomenon, there is considerable confusion around the different types of UHI and their assessment. The Urban Heat ...

In SAR Image Analysis — A Computational Statistics Approach, an accomplished team of researchers delivers a practical exploration of how to use statistics to extract information from SAR imagery. The authors discuss various models, supply sample data and code, and explain theoretical aspects of SAR image analysis that are highly relevant to practitioners and students. ...

Thousands of satellite datasets are freely available online, but scientists need the right tools to efficiently analyze data and share results. Python has easy-to-learn syntax and thousands of libraries to perform common Earth science programming tasks. Earth Observation Using Python: A Practical Programming Guide presents an example-driven collection of basic methods, applications, and visualizations to ...